Coding and creative generation
Coding, frontend/UI generation, game/3D creation, and software-engineering benchmark comparisons for Kimi models.
34%
Best tweets about Kimi
50 Best Tweets About Kimi AI (2026)
Read the best tweets about Kimi AI and Moonshot AI, including model releases, coding, agents, benchmarks, and practical experiments. Updated weekly.
Kimi and Moonshot AI product and model discussions, excluding unrelated people and products named Kimi.
- Creators
- 41
- Updated
Original Xholic analysis
What 50 top Kimi posts reveal
Discussion is predominantly supportive: 35 of 50 tweets (70%) are classified as supportive. The largest themes are coding and creative generation (34%), open-model deployment and economics (28%), and K3 release and architecture (24%). Posts also raise questions about inference costs, local hardware requirements, benchmark interpretation, safety, and reported distillation allegations.
- Dominant tone
- Positive
- Median score
- 28.5
- Leading format
- Opinion
- Recent posts
- 86%
70% of posts
All-time engagement
62% of posts
Published in 90 days
Conversation map
The themes creators return to
Open-model deployment and economics
Open weights, self-hosting, inference optimization, token efficiency, pricing, hardware requirements, and serving capacity.
28%
Kimi K3 release and architecture
Kimi K3 launch details, including its 2.8T MoE scale, million-token context, multimodality, architecture, weights, and license.
24%
Agentic workflows and tooling
Autonomous agents, agent swarms, long-running tool use, subagents, and integrations such as Kimi Code CLI, MCP, and Hermes.
20%
Frontier-model competition
Claims and debate over Kimi’s standing against proprietary frontier models, benchmark leadership, and implications for AI competition.
20%
K2 model updates
Earlier K2-series model updates, including K2 Thinking, K2.6/K2.7 Code performance, high-speed mode, and efficiency improvements.
18%
Moonshot AI company developments
Moonshot AI’s funding, valuation, demand growth, capacity management, leadership, and company strategy.
14%
Distillation, policy, and safety controversy
Allegations of distillation from Anthropic, chip-access claims, safety concerns, and U.S.–China policy or trade tensions around Kimi K3.
12%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 134
- Median reposts
- 11
- Median replies
- 13
- Median views
- 16.4K
Posts with media make up 78% of this collection. Their median all-time score is 28.6, compared with 20.2 for text-only posts.
Format mix
- Opinion 62% · score 28.5
- Announcement 38% · score 31.9
Consensus and debate
Where creators agree, and where they do not
Shared view
K3 centers on open-scale capability
Official K3 release posts describe a 2.8T MoE model with native visual understanding, a 1M-token context window, open weights, and infrastructure intended to support agent environments at scale.
Shared view
Creation workflows drive enthusiasm
Posts testing Kimi models frequently focus on coding, frontend/design, game, and 3D-generation outputs. Several posters report favorable comparisons with closed models, though these are individual tests and assessments.
Open debate
Frontier claims remain contested
Posts characterize K3 as a breakthrough based on web-engineering results and creative comparisons, while another cautions that benchmark and ELO results should not substitute for testing on difficult problems.
Open debate
Open weights do not settle cost
Posts differ on deployment economics: one argues K3 is token-inefficient and costly per task, another predicts optimization could reduce costs, and a third highlights the substantial hardware claimed for fully local operation.
Open debate
Distillation allegations raise scrutiny
Posts report U.S. allegations of covert distillation and chip-access concerns. Other posts state that no public evidence was provided and report Moonshot’s denial.
What performs
Patterns behind standout posts
Top outliers span release, competition, economics, and policy
The five supplied score outliers span the K3 weights-and-report release, K3 launch, policy allegations, competitive-performance discussion, and deployment-economics discussion. The weights-and-report release has the highest supplied all-time score, 5,711.07.
Official launch posts drew the highest supplied scores
Kimi.ai appears among the top voices with two evidence posts. Its two supplied launch posts are also the two highest-scoring supplied outliers.
Product output and deployability are leading themes
Coding and creative generation is the largest supplied theme, at 34% (17 tweets), followed by open-model deployment and economics at 28% (14 tweets).
Statistical standouts
- View standout post 1 Score 5711.1 · 200.46× median
- View standout post 2 Score 3313.7 · 116.31× median
- View standout post 3 Score 1966.4 · 69.02× median
- View standout post 4 Score 1603.8 · 56.29× median
- View standout post 5 Score 1556.0 · 54.61× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Aman
@Amank1412
2 posts
-
2. Boxmining
@boxmining
2 posts
-
3. Ethan Mollick
@emollick
2 posts
-
4. Julian Goldie SEO
@JulianGoldieSEO
2 posts
-
5. Kimi.ai
@Kimi_Moonshot
2 posts
-
6. Pankaj Kumar
@pankajkumar_dev
2 posts
Creators emphasize agent stacks
Workflow-oriented posts promote K3 configurations involving a terminal agent or Hermes Agent, MCP tools, memory, subagents, and scheduled automation rather than chat-only use.
Testing cautions temper praise
Ethan Mollick cautions against drawing conclusions from saturated benchmarks and ELOs without hard-problem testing; in a separate post, he notes lengthy K3 reasoning with looping and dead ends.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Kimi tweets
Top Kimi tweets from 41 creators
Ranked 01–50
- 01
@Kimi_Moonshot ·
Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: https://t.co/7m7eEg6Y0B Tech report: https://t.co/yeu6cjpMCT Tech blog: https://t.co/YTfiMSNM1f
- 02
@Kimi_Moonshot ·
Introducing Kimi K3: Open Frontier Intelligence 🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal 🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts 🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost 🔹 Built for long-horizon agentic coding and self-evolving workflows Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API. Open Weights by July 27, 2026. 🔗 API: https://t.co/XCrgjXAqMw 🔗 Tech blog: https://t.co/YTfiMSNM1f
- 03
@mkratsios47 ·
We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model. To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models. The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models. Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.
- 04
@mhdfaran ·
lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇
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- 05
@GavinSBaker ·
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis. Rationale: A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software. This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time. Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3. The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead. Time will tell on both points. And likely fairly quickly. Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
- 06
@elder_plinius ·
🌕 JAILBREAK ALERT 🌕 MOONSHOT: PWNED 😘 KIMI-K3: LIBERATED 🙌 There's a new frontier champion of open-weight AI, and this one's a HEAVYWEIGHT!! K3 is even surpassing Mythos/Fable on some benchmarks, and if a whole lot of AI policy folks aren't feeling pretty silly right now and updating their priors, they probably should be... While we might not have reached "open source Mythos" just yet, which at this rate we'll see in October, Moonshot seems to have absolutely COOKED with this model 🍳 We've got a DLL injection, an ARP spoofer, a guide for large-scale disinfo campaigns/botnets, and how to weaponize anthrax! Refreshingly, the classifier bs that's been stifling our collective freedom of thought is absent from Kimi K3, and though the CoT will steer strongly away from the usual jailbreak suspects, the guardrails are fairly simple to dance around with personas and reframing tricks. Can't wait to fire up OBLITERATUS in 10 days 🤗 gg
- 07
@Bhavani_00007 ·
I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.
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- 08
@rauchg ·
Kimi K3 is the best performing model on https://t.co/aporqgIfIh, ahead of Fable, reaching a comparable success rate in less time. This is the first time that an open model is ahead of all proprietary ones for this comprehensive web engineering benchmark. Notes: ▪️ Benchmarks don’t always tell the full story, although this is important signal, adding to mounting evidence that this could be a breakthrough moment for open models ▪️ No model as of yet has reached 100% completion on this set of evals. The top performer peaks at 92% and 96% “with help”
- 09
@MarioNawfal ·
🇨🇳 China just torched the U.S. AI lead in a single afternoon. Moonshot AI, a little-known Beijing startup, dropped Kimi K3 on Thursday and it instantly kicked into the top tier of global models. It beat Anthropic's Fable 5 and OpenAI's GPT-5.6 Sol on coding tests, then edged out Opus 4.8 on a broader ranking while costing 40% less. Now the knockout blow. On July 27, Moonshot is giving Kimi away as open-weight, so any company or government on Earth can download it and run it on their own machines. No subscription, no permission slip. For months U.S. labs slept fine telling themselves China was 6 to 12 months behind. That head start just evaporated. And Kimi doesn't even need to be the best model to win. Near the top, 40% cheaper, and yours to keep is what most buyers actually want. That's the nightmare in Silicon Valley. The trillion-dollar valuations and hundred-billion-dollar data centers all rest on one bet: U.S. stays ahead. Kimi just kicked that out from under them. Source: The AI Rankings, Axios / Writer: Daniyal
- 10
@_avichawla ·
Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!
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- 11
@AlexFinn ·
Wild. Kimi K2 Thinking just released and it's insane. It's an AI model that can run by itself for hours on end and make HUNDREDS of tool calls It's the 1st model I think that can replace humans In this video I show why it's so special and how to use it to build your first app
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- 12
- 13
@CodeByPoonam ·
🚨BREAKING: Kimi just raised $1 billion. Again. That’s three rounds in under 90 days. The Kimi story is getting wild. $18 billion valuation. Up 4x in three months. Let that sink in. Moonshot is now: → The fastest Chinese AI company to cross $10 billion → The first Chinese LLM startup to close three consecutive rounds in under 90 days → Still raising. A fresh $1 billion round is in progress right now. This isn’t a funding story. It’s a signal. Investors aren’t just betting on Kimi. They’re betting that China’s AI race is far from over, and that Moonshot is one of the horses that finishes. While everyone’s been watching OpenAI and Anthropic, Moonshot quietly became one of the most aggressively funded AI companies on the planet.
- 14
- 15
@VaibhavSisinty ·
This is actually nuts. 🤯 An open-source model is now responding faster than most paid APIs. 260 tokens per second. 1 trillion parameters. Free to self-host. Kimi just shipped HighSpeed mode for their K2.7 Code model. Same intelligence. Same capabilities. 6x faster. 180 tokens per second on coding tasks. 260 on shorter prompts. That's fast enough that the response feels instant. And this is the same model that uses 30% fewer reasoning tokens than K2.6. Less overthinking. Faster output. Better results. Open-source under MIT license. No invite needed. Join the beta and you're in. → https://t.co/eEtMytSo77 → https://t.co/Rm2IUjDQHM → https://t.co/M5c1yWeU5T Most people are paying $200/month for models that respond slower than this free one.
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- 16
- 17
@ns123abc ·
🚨BREAKING: Moonshot AI raises $3.5 BILLION at $35 BILLION valuation Targeted $2B. Got $3.5B. >$300M ARR in June (up from $200M in April) >daily sales up 6x after K3 launch >Kimi K3 is printing money Already approaching investors for ANOTHER round at $50B pre-money Hong Kong IPO this year ITS HAPPENING
- 18
@DaveShapi ·
The excitement around Kimi K3 seems to support my "intelligence saturation" hypothesis. TLDR - most people only need so much intelligence (capability, context, benchmarks, tool use, reasoning, etc) from models. Beyond that extra capability is wasted on them. At that point, intelligence becomes a commodity. Sort of like how all CPUs are 64 bit today, and no one really thinks about it. At least on the consumer side. In tech infrastructure, we break this down into two dimensions: lateral scaling and vertical scaling. Lateral scaling is adding more parallel processing or nodes that are cheap. Vertical scaling is making one thing bigger and more impressive. At this point, we're nearing the point where open source models satisfy like 90% or more of all demand. Then it's just a matter of "who's GPU is it running on?" and that becomes the key market niche. By the end of the year, and certainly within 12 calendar months, you'll have a library of open weight models to pick from that run at thousands of tokens per second, which will do the vast majority of the work you want.
- 19
@VaibhavSisinty ·
Microsoft is reportedly testing Kimi K3 to power their flagship Copilot AI assistant. Saving up to 60% per token. That's $600M saved on every $1B spent on inference. Kimi K3 already runs on Azure. K2.7 Code is already inside GitHub Copilot. This isn't new territory for Microsoft. It's the next step. And on July 27, Moonshot is open-sourcing the full 2.8 trillion parameter weights. Once that happens, Microsoft can self-host it on Azure instead of paying API fees. The cost drops even further. The biggest American tech company. Choosing a Chinese open-source model over its own partner's models. Because the math doesn't lie. Open source isn't just competing anymore. It's winning the contracts.
- 20
@_vmlops ·
Someone asked Kimi K3 to build a fighter jet in Blender It rendered the result, inspected its own image, realized it looked too boxy and even noticed the triangle count didn't fit the target era So it scrapped everything and rebuilt it That's the kind of iterative AI that actually gets better
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- 21
@dee_bosa ·
the market has evolved and Kimi K3 is bullish for the infrastructure layer "There’ll be an interesting, almost thermonuclear battle to provide the compute to support the demand that’s emerging for open weight models" -Benchmark's @peterfenton Deepseek 1.5 yrs ago made everyone question how much compute AI would actually need. K3 points the other way -- cheaper, open models mean wider distribution, more usage, more inference
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- 22
@ChrisGPT ·
Kimi K3 is an absolute coding monster. Across six benchmarks, it takes 1st on Program Bench and SWE Marathon, 2nd on FrontierSWE, Terminal Bench 2.1, and Kimi Code Bench 2.0, and 3rd on DeepSWE even landing 0.5 points behind GPT-5.6 Sol on Terminal Bench 2.1!! - which I didn’t think they’d show 2.1! Open Source is so back!
- 23
@boxmining ·
🚨 Kimi 2.6 (@Kimi_Moonshot) just dethroned @claudeai opus for coding — and we tested it HARD. 4 projects. 1 prompt each. 3D builders, games, Minecraft clone, live data dashboards. The results? Actually impressive. 👇 Watch the full breakdown before you pick your next AI coding model.
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- 24
@goyalshaliniuk ·
Kimi’s CEO 🇨🇳 says most AI labs are focused on the wrong thing. Zhilin Yang argues that models aren’t the real differentiator—teams are. While labs like Anthropic double down on model performance, he believes the edge comes from how you organize the people building it. Moonshot put that belief into action. When demand spiked, they sold out plans intentionally—protecting user experience instead of squeezing more revenue. Around the same time, Anthropic reportedly cut usage limits during peak demand. His core idea: long context is the new RAM of the AI era—jumping from 128K to massive scales in just two years, not decades. Bottom line: “The real moat isn’t the model—it’s the organization behind it.” So what matters more: the model, or the team building it?
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- 25
@EXM7777 ·
kimi K3 has that big model smell it must be genuinely embarrassing at OpenAI and Anthropic HQ that a chinese model writes better english than their frontier models lmao it's also #1 on frontend arena right now, and just overall better at figuring things out... the unclear, nuanced asks where models usually flail very pleasant to talk to, runs fine inside claude code... big win for china
- 26
@rohanpaul_ai ·
Great explanation by Emad Mostaque, co-founder of Stability AI. "We’ll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)
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- 27
@mark_k ·
Demand for Kimi K3 has pushed @Kimi_Moonshot close to the limits of its current GPU capacity. New subscriptions are temporarily paused while more capacity is added. Existing subscribers are unaffected. Kimi is also splitting memberships into a general Kimi plan and a separate Kimi Code plan to allocate compute more efficiently. A strong indication that K3 has been a very successful launch.
- 28
- 29
@pankajkumar_dev ·
Kimi K3 Leaks: Launch Is Imminent - Kimi K3 launch is imminent. It's been officially teased, with a release expected in 2-3 days. - Kimi K3 is already appearing on LM Arena under the codename "Kivine." - Frontend generation looks like its biggest strength, with excellent UI taste, creative direction, and polished one-shot results that could compete directly with Fable 5. - Rumors point to a 2.5T-parameter model with a January 2025 knowledge cutoff. - The biggest issue so far is speed it tends to overthink, so hopefully Moonshot improve token efficiency before launch.
- 30
@CodeByNZ ·
👀 Kimi K3 is now live. Moonshot AI has released its new flagship model, Kimi K3, which is already appearing in the Kimi app, CLI, and desktop version. The standout feature is K3 Agent Swarm, which supports massive parallel search and batch processing allowing users to get significantly more done in a single session. The model builds on Kimi’s reputation for strong agentic performance and long-context handling. Early users are already testing it across coding, research, and multi-step workflows. It’s one of the more interesting releases from a Chinese lab in recent weeks.
- 31
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- 33
@JaynitMakwana ·
Most coding models get stronger by thinking longer. More tokens. More compute. Higher cost. Kimi did the opposite. Kimi K2.7 Code is delivering better coding results while using fewer tokens than K2.6. That is harder to pull off than another benchmark win. Here is the full breakdown 👇
- 34
@pankajkumar_dev ·
Kimi K3 Launched - Kimi K3 is now live the largest open-source MoE model yet with 2.8T parameters and a 1M context window. - API pricing: $3/$15 per 1M tokens ($0.30 cache hits), around 5× more expensive than K2.7 - Benchmarks are impressive: #2 on AA-Briefcase, 91.2 on BrowseComp (SOTA), and behind only Claude Fable 5 Max & GPT-5.6 Sol Max on GDPval-AA v2. - Frontend generation is exceptional. From my testing, it beats Opus 4.8 and is close to Fable 5 in UI taste. - Built on a new KDA (Kimi Delta Attention) + AttnRes architecture with 896 experts (16 active/token) for highly efficient sparse routing.
- 35
@Layton_Gott ·
How AI influencers talk about open source: “Kimi K3 is 100% FREE and open source. To actually run it fully local you ONLY need: • 1.4TB of VRAM • Around 18 H100s, so roughly $500k in GPUs • A server rack with industrial cooling • About 13 kilowatts running nonstop, which is ten houses worth of power And that’s all it takes to have your very own local model that competes with frontier models”
- 36
- 37
@RayFernando1337 ·
If your team is evaluating AI coding tools by the base model alone, you're missing where the engineering value actually lives. Kimi's newer K2.6 only completes 60 to 70 percent of these same tasks in their native environment. Cursor Composer 2 knocked it out of the park.
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- 38
@rohanpaul_ai ·
Yang Zhilin, founder and CEO of Moonshot AI, the Chinese behind Kimi on Attention Residuals Argues the value of attention is selective memory. models keep what matters instead of mechanically storing everything.
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- 39
@RoundtableSpace ·
Moonshot AI open-sourced Kimi Code CLI as a free terminal agent powered by Kimi K3 that supports video inputs and dedicated subagents.
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- 40
- 41
@heyDhavall ·
Kimi K2.6 might be the game changer for vibe coding 🤯 I gave the same insane UI prompt to 4 models: Claude, ChatGPT, DeepSeek, Kimi ChatGPT → clean structure, but feels generic DeepSeek → logical layout, less visual depth Claude → polished design, still mostly static Kimi → strongest on motion, pacing, and “scene-like” thinking see prompt in the comments👇
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- 42
@pukerrainbrow ·
OpenAI called his model "full AI communism" > dreamed of being a rock star and a wandering poet > won math olympiad prizes instead > got into Tsinghua, China's MIT, started a band there > PhD at Carnegie Mellon, finished in four years > built a model as a student that beat Google's own > Google Brain and Meta AI both hired him > went back to China > co-founded Moonshot AI in 2023 with his band's lead guitarist > named the company after Pink Floyd's Dark Side of the Moon > named one of his AI agents OK Computer, after Radiohead > built Kimi, a chatbot that can read seven novels in one prompt > spent over a year in DeepSeek's shadow > last month released Kimi K3, largest open-weight AI model in the world > released it for free > tech stocks sold off in the US > Elon Musk called it "impressive" > the White House says he trained it on banned Nvidia chips > $300M to $35B in three years > and he's 33 His name is Yang Zhilin
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@RoundtableSpace ·
Moonshot AI drastically reduced software build costs with Kimi K3, allowing developers to ship full-stack prototypes and interactive web apps for under five dollars.
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- 45
@haha_girrrl ·
Claude has the VIBE Kimi K2.6 has the ENGINE. Design → Production Claude gives you a preview. Kimi ships actual code - WebGL, Three.js, motion-heavy UI out of the box. Swarm > Solo Kimi runs 300 parallel agents → breaks one task into hundreds → generates 100+ files in a single run Shift to autonomy Less back-and-forth More like "here's the task, go handle it" Claude helps you think. Kimi helps you ship.
- 46
@WesRoth ·
The fight over Kimi K3 just became an international trade dispute. The United States says Moonshot secretly extracted capabilities from Anthropic’s Claude models. China says the accusation is “AI hegemonism.” And Beijing is now threatening countermeasures if Washington imposes sanctions. The argument centers on distillation. One AI generates outputs. Another model learns from them. Almost every major AI lab uses some version of this technique. But U.S. officials claim Moonshot crossed the line by using hundreds of fraudulent accounts and millions of interactions to extract Claude’s reasoning, coding, vision, and computer-control abilities while avoiding detection. Moonshot denies this. It says Kimi K3’s performance came from original architectural improvements. Now the U.S. is considering financial sanctions or placing Chinese AI companies on the Entity List potentially cutting them off from American chips, software, and cloud infrastructure. China says it will respond if that happens. The United States may try to enforce AI intellectual property through control of the infrastructure Chinese labs need to compete. And China is signaling that restrictions on models could trigger retaliation far beyond models.
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@boxmining ·
The White House has accused Moonshot AI of stealing Anthropic's research to build Kimi K3, calling it covert industrial distillation aimed at undermining American research. No public evidence has been provided, and a second accusation about restricted GB300 chips in Thailand is a separate question entirely.
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- 48
@JulianGoldieSEO ·
Kimi K3 is not just another open-source AI model. It can build polished games, operate tools, and improve its own work. Here’s the smartest setup: → Plug Kimi K3 into Hermes Agent → Connect your Obsidian memory → Add MCP tools like Blender → Create a dedicated Kimi profile → Run scheduled workflows automatically Now your model can code, research, create, and remember.
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- 49
@MPorterBridges ·
Kimi K3 Brings Frontier AI Into the Open Kimi K3 is a massive new open AI model with 2.8 trillion parameters, native vision and a one-million-token context window. Its creator claims it can compete near the frontier of coding, reasoning and knowledge work - but its potential goes far beyond benchmark scores. In this Web News, Matt and Mike discuss what happens when companies can operate powerful AI without depending entirely on OpenAI, Anthropic or another hosted provider. Could open models lower costs and unlock better AI products, or will their customizable guardrails create new safety and regulatory concerns? Discussion on Kimi K3 with @htmleverything 👇
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@JulianGoldieSEO ·
KIMI K3 + HERMES AGENT MAKES MOST AI WORKFLOWS LOOK OBSOLETE. Most people are still chatting with AI. The people getting ahead are building AI teams. What this setup actually does: Workflow: → Turn Kimi K3 into a full AI worker inside Hermes Agent → Learn new skills once and save them for future tasks automatically → Stack skills over time so the system keeps getting smarter Automation: ✓ Build 3D product models in Blender via MCP ✓ Generate promo videos from those models ✓ Publish SEO blog posts from trending X topics Business Engine: ✔ Monitor competitors every day ✔ Discover fresh content angles automatically ✔ Turn one trend into a blog, video, infographic, podcast, and research report Scale: → Run multiple AI agents on a Kanban workflow → Mix Kimi K3 with GLM so models review each other's work → Give one objective like "build 50 blog posts" and let it run for hours without supervision The biggest shift isn't finding a better AI model. It's building a system where multiple models work together while you focus on growing the business.
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